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Record W2796154395 · doi:10.1080/08941920.2018.1450912

Conserving Biodiversity in Farm Animals: Do Farmer and Public Biodiversity Knowledge and Awareness Matter?

2018· article· en· W2796154395 on OpenAlexafffundabout
Albert Boaitey, Ellen Goddard, Getu Hailu

Bibliographic record

VenueSociety & Natural Resources · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of GuelphUniversity of Alberta
FundersGenome AlbertaGoddard Space Flight CenterGenome Canada
KeywordsBiodiversityLivestockSelection (genetic algorithm)Environmental resource managementBusinessAgricultural biodiversityGenetic diversityDiversity (politics)Production (economics)Natural resource economicsBiotechnologyEnvironmental planningGeographyBiologyEcologyPolitical scienceEconomicsEnvironmental healthComputer science

Abstract

fetched live from OpenAlex

Increases in biodiversity losses are a growing concern globally. In farm animals, related concerns about losses in genetic diversity have potentially increased with the emergence of breeding technologies that allow for faster genetic change in herds. Farmer and public acceptance of specific breeding practices can be influenced by a number of factors, including concerns about biodiversity and knowledge of biodiversity. The link between these factors and acceptance of new genetic technologies, if it exists, may help explain concerns about genetic technologies. This article examines the effect of attitudes and knowledge about biodiversity on the acceptance of genomic selection in livestock production using farmer and public survey data from Canada. Our results suggest that the link between biodiversity concerns and the acceptance of genomic selection is more robust for the public than for farmers. We also find that biodiversity knowledge and attitudes have opposite effects on acceptance of genomic selection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.252
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2018
Admission routes3
Has abstractyes

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